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%252C+where+we+devise+a+generative+Bayesian+network+to+produce+diverse+synthetic+scenes+with+real-world+patterns%252C+and+conduct+pre-training+with+a+joint+objective.+By+jointly+learning+a+coarse-to-fine+contrastive+learning+task+and+an+occlusion-aware+reconstruction+task%252C+the+model+is+primed+with+transferable%252C+geometry-informed+representations.+Post+pre-training+on+synthetic+data%252C+the+acquired+knowledge+of+the+model+can+be+seamlessly+transferred+to+two+principal+downstream+tasks+associated+with+3D+scene+understanding%252C+namely+3D+object+detection+and+3D+semantic+segmentation%252C+using+real-world+benchmark+datasets.+A+thorough+series+of+experiments+robustly+display+our+method%27s+consistent+superiority+over+existing+state-of-the-art+pre-training+approaches.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.dfddc14aac1f46153ee61c5192206cab%3frik%3dX6AnvbgpYr%252fNvw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Generalizability+Vector+Icon&FORM=IRPRST&ck=CAA5CFBED75DAFE395B860350D16B17B&selectedIndex=18&itb=0)
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